[S] tech report on visualizing non-linear functions

Tony Plate (Tony.Plate@mcs.vuw.ac.nz)
Wed, 05 Aug 1998 21:37:34 +1200

I've written some S+ functions which allow one to vizualize
a general non-linear function of many inputs (by generalizing
GAM-style plots). A report which describes the vizualization
method is available from my web site

The method can be applied to any fitted statistical model
which is capable of generating predictions at new data
points. If anyone is interested in trying it out, let me
know and I'll email the S+ functions.

Tech Report:

Visualizing the function computed by a feedforward neural network

Tony Plate (Victoria University of Wellington)
Joel Bert (University of British Columbia)
John Grace (University of British Columbia)
Pierre Band (Health Canada)

Computer Science Technical Report CS-TR-98-5
Victoria University of Wellington, Wellington, New Zealand

A method for visualizing the function computed by a
feedforward neural network is presented. It is most
suitable for models with continuous inputs and a small
number of outputs, where the output function is reasonably
smooth, as in regression or probabilistic classification
tasks. The visualization makes readily apparent the
effects of each input and the way in which the functions
deviates from a linear function. The visualization can
also assist in identifying interactions in the fitted
model. The method uses only the input-output relationship
and thus can be applied to any predictive statistical
model, including bagged and committee models, which are
otherwise difficult to interpret. The visualization method
is demonstrated on a neural-network model of how the risk
of lung cancer is affected by smoking and drinking.


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